As introduced in Section REF , the existing defense strategies focus on classification models, ranging from model level to data level. As data are not accessible in FL, model level defense is desired, where a model level detector is built to find the adversarial behavior and refrain it from training with others [1]}, known as malicious detection. Apart from detection, robust training is another approach that refines training protocol [2]}. To the best of our knowledge, no existing study addresses the defense for FedGAN. In this section, we introduce an efficient and effective defense method, FedDetect.
